Xiaofeng Guo

Papers

Total Citations

H-Index

About

Xiaofeng Guo is a rising researcher at the forefront of human–robot interaction (HRI), specializing in gesture recognition for dynamic, real-world environments. His work tackles a critical gap in robotics: enabling fast, natural, and multimodal communication between humans and machines. Guo’s major contributions center on developing lightweight, attention-based neural architectures that process skeletal data with high efficiency. His most-cited paper, "A Fast-Response Dynamic-Static Parallel Attention GCN Network for Body–Hand Gesture Recognition in HRI" (2023, 12 citations), introduces a novel parallel attention mechanism that dramatically accelerates recognition speed—a key requirement for safe, responsive HRI in generic scenes. Building on this, his 2025 work, "A Lightweight Spatio-Temporal Skeleton Attention Transformer for Long-Distance Gesture Recognition in UAV Control" (1 citation), pioneers solutions for the underexplored challenge of outdoor, long-distance gesture control, addressing issues of signal degradation and computational constraints. Though early in his career, Guo’s focus on practical, deployable AI for robotics—from factory floors to drone operations—marks him as a promising innovator. His research not only advances algorithmic efficiency but also expands the boundaries of where and how humans can intuitively command machines.